1. Field of the Invention
The present invention relates to the field of data mining and analysis and, more specifically, to methods and systems relating to utilizing content, dynamic patterns, and/or relationship information in data analysis.
2. Description of Related Art
Recent expansion of the availability and amount of information that people throughout the world are exposed to and have available to them is incredible. For example, information available on computer networks, e.g., intranets, the Internet, email, cellular networks, and other sources of digital media, have provided people access to an immense wealth of information. In fact, the amount of information available to people and organizations is starting to overwhelm people and organizations. Further, more people and organizations rely more-and-more on the Internet and various information networks to obtain information. As a result, it is desirable to have tools that help manage the massive amount of information available to people and organizations and to analyze and provide desired information or results from that analysis that is more focused, concise, and better suited for their needs. For example, it may be desirable to have method(s) and system(s) that may automatically predict, identify and/or recommend information (e.g., items, documents, etc.) that people or organization may find more or most useful. Therefore, a number or method(s) and system(s) have been developed that help to manage the massive amount of information available by, for example, analyzing the information available and providing recommendations.
Recommendation method(s) and system(s) have become a particularly important area since the appearance of the collaborative filtering in the mid 1990s. Examples of various applications for recommendation method(s) and system(s) include recommending books, CDs, and other products at, for example, Amazon.com, recommending movies by MovieLens, etc. There are also a number of methods and systems that provide personalized recommendations, content, and services to users. Some examples include those describe in U.S. Pat. Nos. 6,266,649, 6,912,505, and 6,853,982, and U.S. Patent Application No. US20050198056.
Content-based filtering, collaborative filtering, and hybrid approaches are three exemplary recommendation systems. Some examples of these approaches are described in D. Pierrakos, G. Paliouras, C. Papatheodorou, and C. Spyropoulos, Web usage mining as a tool for personalization: A survey. User Modeling and User-Adapted Interaction, 13:311-372, 2003; G. Adomavicius, and A. Tuzhilin, Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions, IEEE Trans. Knowl. Data Eng. 17(6), 734-749, 2005; and M. Balabnovic and Y. Shoham, Content-Based, Collaborative Recommendation, Communications of the ACM, March 1997. Although these approaches give some reasonable results, there is room for improvement in the systems and methods of data analysis and particularly with respect to providing recommendations.
The prior systems and methods lack certain useful capabilities. For example, prior recommendation systems and methods typically do not consider the dynamic nature of both information (e.g., the change in value of information over time) and personal interests (e.g., changing interests of a user or entity). The importance of items and the interests of users are both naturally dynamic and change over time. Therefore, there is a need for recommendation systems and methods that can make recommendation(s) based on, for example, the dynamic nature of an item of interest and/or formal or informal communities to which user(s)/entity(ies) are a part of, or interested in.